Preface to the focus section on environmental seismology
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As seismic data are increasingly used to investigate a diverse range of subsurface phenomena beyond regular fast-rupturing earthquakes (Peng and Gomberg, 2010; Beroza and Ide, 2011), it is important to acknowledge that human-generated ground vibrations may be mistaken for naturally generated subsurface processes (Larose et al., 2015; Li et al., 2018). Correct discrimination of natural processes from anthropogenic noise is especially pressing given the trend in seismic detection research toward automated algorithms and machine learning methods (Yoon et al., 2015; Kong et al., 2019;Mousavi and Beroza, 2022) and the growth in seismic data collection in new environments such as urban and industry settings (e.g., Díaz et al.,2017).
Laboratory testing of digitizers and seismometers helps ensure that prior to deployment the instrumentation can produce high quality data and is operating within specifications. In this work we detail the software package called: the Albuquerque Seismological Laboratory (ASL) Digitizer Test Suite. This Java software package provides several algorithms to verify various performance parameters of digitizers commonly used for recording analog seismic instruments. The goal of these tests is not to be exhaustive, but to identify common failures that could compromise the integrity of seismic data being recorded on the digitizer. For example, Sandia National Laboratories (e.g., Slad and Merchant, 2018) routinely do comprehensive testing of digitizers for various monitoring missions. While these tests reports are valuable for comprehensively characterizing a recording system, it would be resource intensive to conduct such tests on every seismic recorder used in a network. We focus on tests that include ways to estimate the sensitivity, timing, self-noise, and clip-level of the digitizer, as well as the fidelity of the signal being recorded. The software is publicly available and provides a way for the community to verify the integrity of a digitizer using a minimum amount of outside equipment.
We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault‐plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault‐plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.
Ground failure due to earthquake-induced liquefaction, surface fault rupture, and landsliding has caused substantial damage in the past and continues to pose risks to critical infrastructure in the future. The consideration of ground failure is central to engineering analysis and design, ranging from site-specific engineering assessments of ground failure potential to broader regional-scale ground failure assessments of near-real-time earthquake impacts or longer-term portfolio risk. Uncertainties in all aspects of the problem complicate the prediction of earthquake-induced ground failure, including the identification of hazard-susceptible geomaterials, characterizing their spatial extent and physical properties, spatio-temporal variability in groundwater conditions, characterization of earthquake ground motions, assessing ground failure severity, and linking ground failure severity to infrastructure damage and loss.
The U.S. Geological Survey’s Geomagnetism Program is collaborating with the Earthquake Hazards Program and Global Seismographic Network Program to densify magnetic field observations. This collaboration focuses on the installation of magnetometers, or magnetic variometers, at existing seismic stations. Along with improving the density of space weather observations for hazard monitoring, these data can be used to correct colocated magnetic field induced noise in seismic data. Such corrections are especially useful during time periods of large magnetic storms where the magnetic field‐induced instrument noise can be of similar amplitude to earthquake ground‐motion records.
Microelectromechanical system (MEMS) accelerometers are useful for seismological and engineering applications because of their ability to record unsaturated large seismic signals. Recent advances in MEMS technologies enable the design of instruments with improved capabilities that also allow the recording of small signals. As a result, MEMS can be useful across a broad dynamic range and for both major earthquakes and smaller magnitude events. Here, we analyze the performance of a MEMS‐based accelerometer with the capability of real‐time data transmission. We discuss the self‐noise level, dynamic range, and sensitivity, along with the timing precision and data transmission latency, and compare these parameters to other MEMS‐based instruments. We also summarize observations from a regional network deployed in southern Mexico over a period of 3+ yr for the purpose of earthquake early warning. In addition to the sensor evaluation, we present a large, openly available data set of strong motion data from the Mexican network that comprises continuous ground‐motion records from 24 accelerometers in the period between 2017 and 2022.
Detecting and cataloging seismic events are among the most fundamental tasks in seismology. Many standardized tools for these tasks exist, including the open‐source package repeating earthquake detector in Python (REDPy). REDPy generates an organized catalog of seismic events from continuous waveform data, in which events are automatically separated into groups (“families”) by their waveform similarity through cross‐correlation. REDPy also automatically generates various outputs that allow a user to visualize important trends in the catalog, which may be used in real time or in retrospective analyses to allow rapid identification of interesting features. The code was designed for near‐real‐time volcano monitoring but is applicable across a broad range of use cases in seismology and seismoacoustics. In this article, the utility and performance of REDPy are demonstrated on two highly seismogenic volcanic eruption sequences: the onset of the dome‐building eruption of Mount St. Helens, Washington, from 2004 to 2005, and the entirety of the summit caldera collapse sequence of Kīlauea, Hawai‘i, in 2018. This article is meant to be a companion to the documentation of the code; in addition to detailing the basic required inputs, script functionality, and resulting outputs, the reasonings behind several important design decisions are also discussed.
We describe automated ground‐motion processing software named gmprocess that has been developed at the U.S. Geological Survey (USGS) in support of near‐real‐time earthquake hazard products. Because of the open‐source development process, this software has benefitted from the involvement and contributions of a broad community and has been used for a wider range of applications than was initially envisioned. Here, we give an overview and introduction to the software, including how it has leveraged other open‐source libraries. We highlight some key features that gmprocess provides, compare response spectra calculated with the automated processing approach of gmprocess to the response spectra provided by the Next Generation Attenuation projects, and summarize projects that have utilized gmprocess. These use‐cases demonstrate that this software development effort has been successfully leveraged in earthquake research activities both within and outside the USGS.
The Fairbanks region of central Alaska is part of a broad zone of intraplate crustal deformation, situated north of the Denali fault and north of the ongoing collision and flat‐slab subduction of the Yakutat oceanic plateau. Seismicity in the Fairbanks region occurs both in diffuse areas as well as in well‐defined lineaments, such as the left‐lateral Salcha fault, which hosted the 1937 M 8 7.3 earthquake. Starting with the regional seismicity catalog, we perform waveform cross‐correlation, network‐matched filtering, and relative relocation to obtain an enhanced seismicity catalog over the time period 2014–2024. Based on the relocated catalog, we interpret a set of 15 fault segments, including two conjugate faults and two new faults east of the previously documented fault system. Considering the combined seismicity in the Minto and Fairbanks regions, the median depth of seismicity decreases from east (6 km) to west (20 km). Our interpreted faults provide guidance for future tectonic modeling and assessment of seismic hazards in this region.
Raw seismological waveform data contain noise from the instrument’s surroundings and the instrument itself that can dominate recordings at low and high frequencies. To use these data in ground‐motion modeling, the effects of noise on the signals must be reduced and the signals’ usable frequency range identified. We present automated procedures to efficiently reduce low‐frequency noise that are implemented in the software package gmprocess. These procedures check for, and as needed remove, low‐frequency artifacts in the displacement record using polynomial fits, which can be used in combination with existing signal‐to‐noise ratio (SNR)‐based corner‐frequency selection procedures. The automated selections are then efficiently verified and refined using a graphical user interface (GUI) that plots relevant ground‐motion time series and spectra and tracks modifications to signal processing parameters. We demonstrate these procedures using recordings from the 2020 M 5.1 Sparta, North Carolina, and the 2013 M 4.7 southern Ontario earthquakes. Data processed with the SNR‐only and polynomial criteria for these events contain displacement artifacts in 37% and 23% of processed traces, respectively. Records with remaining artifacts are corrected manually using the GUI. These processing steps illustrate the workflow for efficient data processing with quality control.
We apply a machine learning (ML) earthquake detection technique on over 21 yr of seismic data from on‐continent temporary and long‐term networks to obtain the most complete catalog of seismicity in Antarctica to date. The new catalog contains 60,006 seismic events within the Antarctic continent for 1 January 2000–1 January 2021, with estimated moment magnitudes (Mw ) between −1.0 and 4.5. Most detected seismicity occurs near Ross Island, large ice shelves, ice streams, ice‐covered volcanoes, or in distinct and isolated areas within the continental interior. The event locations and waveform characteristics indicate volcanic, tectonic, and cryospheric sources. The catalog shows that Antarctica is more seismically active than prior catalogs would indicate, examples include new tectonic events in East Antarctica, seismic events near and around the vicinity of David Glacier, and many thousands of events in the Mount Erebus region. This catalog provides a resource for more specific studies using other detection and analysis methods such as template matching or transfer learning to further discriminate source types and investigate diverse seismogenic processes across the continent.
The use of fiber‐optic sensing systems in seismology has exploded in the past decade. Despite an ever‐growing library of ground‐breaking studies, questions remain about the potential of fiber‐optic sensing technologies as tools for advancing if not revolutionizing earthquake‐hazards‐related research, monitoring, and early warning systems. A working group convened to explore these topics; we comprehensively examined the application of fiber optics in various aspects of earthquake hazards, encompassing earthquake source processes, crustal imaging, data archiving, and technological challenges. There is great potential for fiber‐optic systems to advance earthquake monitoring and understanding, but to fully unlock their capabilities requires continued progress in key areas of research and development, including instrument testing and validation, increased dynamic range for applications focused on larger earthquakes, and continued improvement in subsurface and source imaging methods. A key current stumbling block results from the lack of clear data archiving requirements, and we propose an initial strategy that balances data volume requirements with preserving key data for a broad range of future studies. In addition, we demonstrate the potential for fiber‐optic sensing to impact monitoring efforts by documenting the data completeness in a number of long‐term experiments. Finally, we outline the features of a instrument testing facility that would enable progress toward reliable and standardized distributed acoustic sensing data. Overcoming these current obstacles would facilitate progress in fiber‐optic sensing and unlock its potential application to a broad range of earthquake hazard problems.
The U.S. Geological Survey (USGS) periodically releases updates to National Seismic Hazard Model (NSHM) for the United States and its territories leveraging current scientific knowledge and methodologies to guide public policy, building codes, and risk assessments regarding potential ground shaking due to earthquakes that may result in infrastructure damage. In subduction zones, there is a need to separate the earthquake catalog into tectonic regimes to create specific seismicity models for which the most appropriate ground‐motion models are then applied. Here, we describe newly developed methods and software, called CatSep, that classifies subduction zone events into three primary tectonic regimes: crustal, interface, and intraslab. This method incorporates information about the location of the earthquake relative to the subducting slab, the depth of the Mohorovičić discontinuity, and the earthquake’s moment tensor. Applying this method is a first step in the NSHM workflow for regions covering U.S. subduction zones. Results using this subduction zone earthquake catalog separation tool for the 2025 Puerto Rico and U.S. Virgin Islands NSHM earthquake catalog are presented and analyzed.
Among the colorful local lore in the Charleston, South Carolina, area, are a number of ghost stories, shared not only over campfires but also in published books. Among the most well-known of the stories is the tale of the Summerville Light. Local lore holds that a strange light sometimes seen in a remote area is a lantern carried by the ghost of a local woman who once waited hours for her husband, who turned out to have been decapitated earlier that day in a train accident (DePoppe, 2023). Extant sources suggest the ghost stories began to circulate in the 1950s to 1960s. So pervasive was the lore that (Old) Sheep Island Road became known among local residents as Light Road, with a local stretch of road known today as Old Light Road. Reviewing the location where the lights appear as well as the nature of accounts, I suggest that many if not all of the anecdotal observations can be most readily attributed to natural phenomena, including earthquake lights from earthquakes that were too small to be felt. Accounts of lights near Summerville cluster in proximity to the generally accepted epicenter of the 1886 Charleston, South Carolina, earthquake, where foreshocks to the 1886 mainshock were apparently concentrated, and within a few kilometers of three M3.5 – 4.4 earthquakes in 1959 and 1960.
Macroseismic data are a key resource to investigate shaking and damage from preinstrumental and early instrumental eras. However, data are often stored as inconsistently formatted reports describing observed shaking and damage, making manually parsing and interpreting accounts labor‐intensive. We introduce a novel workflow using Google’s Gemini 2.5 Pro large language model (LLM) to automate the extraction and structuring of macroseismic observations from summary reports. We apply this workflow to the 22 March 1957 M 5.3 Daly City, California, earthquake as a case study. We used Gemini to extract addresses, originally assigned modified Mercalli intensity values, and descriptions from each report. To address coordinate precision limits, addresses were geocoded via Google’s Geocoding application programming interface. This workflow yielded over 2300 geocoded intensity reports for the Daly City earthquake. We use the geocoded accounts, with the original report intensity assignments, to develop a shaking intensity map that in some respects rivals modern Did You Feel It? Maps. We also extract and present data for the 9 February 1971 M L 6.7 Sylmar, California, earthquake. Our results demonstrate the potential of LLMs for reliably extracting and analyzing large, unstructured macroseismic datasets. LLMs offer a scalable solution for rapidly digitizing macroseismic archives, enabling their broader use to constrain ground‐motion models in modern seismic hazard analysis and to improve our understanding of site effects in urban areas. The concepts explored here may also be applied to the handling of other legacy seismological and earth science data.
The Delaware Basin region of west Texas and southeast New Mexico has become one of the most prolific regions of seismic activity in the continental United States due to widespread hydraulic fracturing and wastewater disposal injection. In response to the increased number of earthquakes in this region, rapid and accurate characterization of earthquake sources is necessary to understand the evolution of seismic activity and level of seismic hazard associated with these earthquakes. This study re-evaluates earthquake magnitudes, estimating moment magnitude (MW) for small earthquakes in the Delaware Basin using 1) moment-rate spectra derived from S-wave coda envelopes, and 2) a relative magnitude method that relies exclusively on the ratio of waveform amplitudes between highly correlated waveform pairs. The coda-envelope method produces accurate M W estimates for small earthquakes ( M 1.5 – 3) that are consistent with independent, waveform modeled moment magnitudes for events with M W > 3 . Using the relative amplitudes method to extend these M W magnitudes to many other events, we successfully provide relative moment magnitude ( M W,rel ) values for 81% of the Texas Seismological Network catalog in the Delaware Basin region, and 45% of the USGS Induced Seismicity Project’s catalog of events in southeast New Mexico. The adoption and integration of the calibrated M W,rel method with current magnitude estimation methods offers valuable insights into the relationships between local and moment magnitude and will contribute to improved characterization of widespread induced seismicity.
Several onshore faults in southern Puerto Rico have recently been recognized as Quaternary active. However, the kinematics of these faults, particularly any lateral component, remain largely unconstrained. It is difficult to characterize low strain‐rate faults, partially due to extensive erosional and anthropogenic landscape modification, steep relief, and frequent landsliding, limiting the preservation of geomorphic features that could serve as recorders of fault motion. Here, we constrain the kinematics along sections of the Great Southern Puerto Rico Fault Zone (GSPRFZ) on the southern coastal plain of Puerto Rico. We integrate ∼1‐m‐resolution light detection and ranging (lidar)‐derived topography, historical air photos, and field mapping to identify a series of ∼50–1200‐m‐long fault scarps and lineaments that trend northwest–southeast and extend for ≥25 km across the southern coastal plain. Fault scarps are primarily south facing, cut across topography, and displace Quaternary deposits and landforms. We document multiple offset geomorphic markers, including channel thalwegs and interfluves formed in deposits previously mapped as Quaternary piedmont alluvial plain. We observe both vertical (south‐side‐down) and right‐lateral meter‐scale displacements, which indicate that the GSPRFZ accommodates right‐lateral oblique motion in the late Pleistocene, consistent with northeast motion of the Puerto Rico and the Virgin Islands microplate away from the Hispaniola block.